docstr: Predict input data
  
  :param data: data to be predicted.
  :param batch_size: batch size per thread. Default: 4.
  :param feature_cols: list of feature column names if input data is Spark DataFrame.
  :return: predicted result.
   If input data is XShards or tf.data.Dataset, the predict result is a XShards,
   and the schema for each result is: {'prediction': predicted numpy array or
    list of predicted numpy arrays}.
docstr_tok: ["Predict","input","data",":","param","data",":","data","to","be","predicted",".",":","param","batch_size",":","batch","size","per","thread",".","Default",":","4.",":","param","feature_cols",":","list","of","feature","column","names","if","input","data","is","Spark","DataFrame",".",":","return",":","predicted","result",".","If","input","data","is","XShards","or","tf.data.Dataset",",","the","predict","result","is","a","XShards",",","and","the","schema","for","each","result","is",":","{","'prediction","'",":","predicted","numpy","array","or","list","of","predicted","numpy","arrays","}","."]
code: def predict(self, data, batch_size=4, feature_cols=None):
      """
      Predict input data
  
      :param data: data to be predicted.
      :param batch_size: batch size per thread. Default: 4.
      :param feature_cols: list of feature column names if input data is Spark DataFrame.
      :return: predicted result.
       If input data is XShards or tf.data.Dataset, the predict result is a XShards,
       and the schema for each result is: {'prediction': predicted numpy array or
        list of predicted numpy arrays}.
      """
      raise NotImplementedError
  
file_path: analytics-zoo/pyzoo/zoo/orca/learn/spark_estimator.py
imports: from abc import abstractmethod
  from zoo.orca.learn.base_estimator import BaseEstimator
label_desc: Use image node_id 2 for calling the Estimator obj's underlying member method code with example usage: obj.predict(data, batch_size, feature_cols) without return types